Video Doorbells

[sender]

Ehm... noob here again... let's say:
I have homeassistant supervised (ability to add addons). I have option to easiliy spin up a VM. Where do I start?

As you know, I am not a Linux guru and I would need every step documented

[rafale77]

Unfortunately, I can't help much in this case. Using the supervised installation means you have everything containerized and I am a bit allergic to this approach as it complicates the interaction between containers and makes access/changes to various code very difficult. It also an inefficient installation mode. These changes are not addons. They are core code changes which will require any installation but the supervised/containerized ones.

[rafale77]

@sender

One idea I just had to simplify things is just to install home assistant from my branch by doing this from a venv:

pip3 install git+https://github.com/rafale77/core.git

But again it does not help the containerized installation which has the disadvantage of its benefit: Contained controlled small environment so you cannot mess with it means that you can't modify anything in it.

[sender]

Hm that's a pity. I think most users have a "supervised" install due to simplicity.

Also interesting:

Fawkes: Protecting Personal Privacy against Unauthorized Deep Learning Models (USENIX Security 2020)

[rafale77]

Yeah I look at things from an overall standpoint... Yes it may appear simple at the surface but it is actually very complicated just one layer underneath so if one wants to do anything more with it, it becomes a hot mess. I use some containers but only for very small and limited applications which benefit from being self contained. For anything more than that... like home-assistant, I just find it absurd.

And yup, privacy... again why I am not running anything like this through a cloud API...
Another example of something made simple but using a very complex solution under the surface to a simple problem. I see the cloud as philosophically the same as the docker containers... Adding layers of complexity and liabilities for the sake of making it convenience.
I am a big advocate of KISS but from an overall standpoint. Not from just the user installation which is only a short one time extra effort vs. a lifetime of inefficiency and other risks.

[sender]

@sender said in Object Recognition:

Ehm... noob here again... let's say:
I have homeassistant supervised (ability to add addons). I have option to easiliy spin up a VM. Where do I start?

As you know, I am not a Linux guru and I would need every step documented

Ok, from another standpoint. You know my setup... can I do something with a vm on ubuntu and having it integrated in hass?

[rafale77]

@sender

Yes you can. I don't know what this entire obsession with containers is about in the home assistant forum. I found it actually much easier to install and manage with a VM or a virtual environment. By the way, the supervised installation has had an attempt for deprecation causing a huge thread on their forum. A lot of people use it but it is so complicated and takes so much work to maintain that the devs wanted to take it away... also because IMHO it really made 0 sense to begin with.

Disturbingly the simplest, fastest, most flexible and easiest installation is the one they recommend for developers:

Home Assistant

Installation

Install Home Assistant

In my case, in a ubuntu VM, I even skip the virtual environment which is an unnecessary added complication.

Make a copy of your home-assistant configuration folder. (If you don't want to start from scratch)
Set up the VM to the same address as your previous installation.
Just install a virgin ubuntu OS, install python3.7 if it isn't already installed, this will depend on the version of ubuntu you installed. I think any version before 20.04 will need it:

sudo apt-get update && sudo apt-get install python3.7

Copy your old .homeassistant folder into your user directory again only if you want to keep your config.
Install home assistant from my repo

python3.7 -m pip install git+https://github.com/rafale77/core.git

and start it:

hass --open-ui

You can go through another tutorial to make it auto start upon VM start but this should suffice for now.
I would argue that this isn't any more complicated than the supervised installation. To me it is actually much simpler... It is literally 3 commands in the worst case for a new install, without any funky docker software to download.

The docker container, I think helped people setup the autostart and install the right version of python... A complicated solution to a simple problem. Adding another layer of management program, virtualization, restrictions, file system management, cpu and memory inefficiency etc... just to control 5 lines of startup code?

[rafale77]

And... now that I have switched to pytorch for facial detection/recognition I am looking to see if Yolov4 can be enhanced and sure enough... a 7 days old update to this project could be what I will try to implement in home assistant next:

GitHub - WongKinYiu/PyTorch_YOLOv4: PyTorch implementation of YOLOv4

PyTorch implementation of YOLOv4. Contribute to WongKinYiu/PyTorch_YOLOv4 development by creating an account on GitHub.

It combines YoloV4 with some of the enhancements of YOLOv5 and seems to be better than both.

[rafale77]

So it seems I am practically done with my setup and it is now running super efficiently and very "false positive" immune.
I have a set of 16 cameras, running on an NVR which record upon motion on my NAS.
I got the system to now eliminate false detection of spider webs and wind blowing leaves triggering motion detection using the object detection scheme. This however is a bit too heavy in terms of computation and therefore power consumption so I have made the whole thing smarter and less wasteful:

Camera motion detection -> trip a sensor on openLuup which then runs a scene -> turn on object detection on Home Assistant (I modified home assistant to have a switch to turn it on and off) -> if object detection sees a recognizable object -> trips another sensor on openLuup which triggers other scenes depending on what object is detected.

I did the same thing with facial detection: Camera motion detected -> trip openLuup sensor -> trigger scene to turn on a virtual switch on openLuup -> which turns on Face recognition on Home Assistant -> If face is detected -> Trip another sensor on openLuup with the name of the detected face -> triggers others scenes which parses the name and unlock doors, turn on lights, sends notifications etc... after some delay, the virtual switch turns back off which turn off the face recognition on home assistant. Been running the whole setup for a month now an its been solid though I have been tweaking things here and there.

A lot of work and research has gone into finding the right AI models and how to implement them on python some with a lot of code optimization... By the way this is an updated version of the one in my previous post, better accuracy.

GitHub - WongKinYiu/ScaledYOLOv4: Scaled-YOLOv4: Scaling Cross Stage Partial Network

Scaled-YOLOv4: Scaling Cross Stage Partial Network - WongKinYiu/ScaledYOLOv4

[CatmanV2]

Just wow

Want to come and install for me?

C

[rafale77]

Yeah a bit far Though I am sure I would enjoy it.